Overview

Dataset statistics

Number of variables13
Number of observations60.015
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 MiB
Average record size in memory104.0 B

Variable types

Numeric10
DateTime1
Categorical2

Warnings

Brændolie forbrug is highly correlated with Produktion til byen and 1 other fieldsHigh correlation
Produktion til byen is highly correlated with Brændolie forbrug and 1 other fieldsHigh correlation
Gram pr. kWh is highly correlated with ElvirkningsgradHigh correlation
Elvirkningsgrad is highly correlated with Gram pr. kWhHigh correlation
Produktion total is highly correlated with Brændolie forbrug and 1 other fieldsHigh correlation
City is highly correlated with DistriktHigh correlation
Distrikt is highly correlated with CityHigh correlation
df_index has unique values Unique

Reproduction

Analysis started2021-03-08 21:32:56.050964
Analysis finished2021-03-08 21:33:05.827303
Duration9.78 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct60015
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50409.34993
Minimum1
Maximum103138
Zeros0
Zeros (%)0.0%
Memory size469.0 KiB
2021-03-08T18:33:05.905541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3964.7
Q126141.5
median50913
Q371927.5
95-th percentile98324.3
Maximum103138
Range103137
Interquartile range (IQR)45786

Descriptive statistics

Standard deviation29187.76089
Coefficient of variation (CV)0.5790148243
Kurtosis-1.006423527
Mean50409.34993
Median Absolute Deviation (MAD)22749
Skewness0.1417743328
Sum3025317136
Variance851925386
MonotocityStrictly increasing
2021-03-08T18:33:05.996423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
566891
 
< 0.1%
791801
 
< 0.1%
1017231
 
< 0.1%
218561
 
< 0.1%
177621
 
< 0.1%
198111
 
< 0.1%
300521
 
< 0.1%
321011
 
< 0.1%
75291
 
< 0.1%
Other values (60005)60005
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
ValueCountFrequency (%)
1031381
< 0.1%
1031371
< 0.1%
1031361
< 0.1%
1031351
< 0.1%
1031341
< 0.1%

Brændolie forbrug
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1511
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean386.2192785
Minimum37
Maximum2118
Zeros0
Zeros (%)0.0%
Memory size469.0 KiB
2021-03-08T18:33:06.083733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile115
Q1195
median272
Q3486
95-th percentile1030
Maximum2118
Range2081
Interquartile range (IQR)291

Descriptive statistics

Standard deviation289.1996441
Coefficient of variation (CV)0.7487965004
Kurtosis2.082280573
Mean386.2192785
Median Absolute Deviation (MAD)113
Skewness1.584643528
Sum23178950
Variance83636.43417
MonotocityNot monotonic
2021-03-08T18:33:06.164431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
213260
 
0.4%
205257
 
0.4%
211250
 
0.4%
207247
 
0.4%
210245
 
0.4%
222245
 
0.4%
216243
 
0.4%
204242
 
0.4%
215240
 
0.4%
223240
 
0.4%
Other values (1501)57546
95.9%
ValueCountFrequency (%)
371
 
< 0.1%
432
< 0.1%
481
 
< 0.1%
493
< 0.1%
542
< 0.1%
ValueCountFrequency (%)
21181
< 0.1%
20801
< 0.1%
20211
< 0.1%
19711
< 0.1%
18892
< 0.1%

Produktion til byen
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4808
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1215.517471
Minimum45
Maximum7201
Zeros0
Zeros (%)0.0%
Memory size469.0 KiB
2021-03-08T18:33:06.244691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile221
Q1483
median786
Q31605
95-th percentile3693
Maximum7201
Range7156
Interquartile range (IQR)1122

Descriptive statistics

Standard deviation1074.743537
Coefficient of variation (CV)0.8841860054
Kurtosis1.834940315
Mean1215.517471
Median Absolute Deviation (MAD)421
Skewness1.555432012
Sum72949281
Variance1155073.67
MonotocityNot monotonic
2021-03-08T18:33:06.332813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59178
 
0.1%
56975
 
0.1%
56075
 
0.1%
59675
 
0.1%
64870
 
0.1%
55868
 
0.1%
60967
 
0.1%
59066
 
0.1%
61266
 
0.1%
61166
 
0.1%
Other values (4798)59309
98.8%
ValueCountFrequency (%)
451
< 0.1%
1151
< 0.1%
1171
< 0.1%
1192
< 0.1%
1212
< 0.1%
ValueCountFrequency (%)
72011
< 0.1%
67321
< 0.1%
66271
< 0.1%
60291
< 0.1%
59931
< 0.1%

Gram pr. kWh
Real number (ℝ≥0)

HIGH CORRELATION

Distinct39791
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.2053872
Minimum129.2684
Maximum799.2819
Zeros0
Zeros (%)0.0%
Memory size469.0 KiB
2021-03-08T18:33:06.426454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum129.2684
5-th percentile205.28301
Q1239.6253
median260.7339
Q3286.3636
95-th percentile334.8252
Maximum799.2819
Range670.0135
Interquartile range (IQR)46.7383

Descriptive statistics

Standard deviation47.49739073
Coefficient of variation (CV)0.1790966285
Kurtosis17.80997664
Mean265.2053872
Median Absolute Deviation (MAD)22.7992
Skewness2.205562428
Sum15916301.32
Variance2256.002126
MonotocityNot monotonic
2021-03-08T18:33:06.512291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280230
 
0.4%
258.461565
 
0.1%
31556
 
0.1%
24052
 
0.1%
25246
 
0.1%
265.263246
 
0.1%
33644
 
0.1%
262.541
 
0.1%
305.454638
 
0.1%
296.470638
 
0.1%
Other values (39781)59359
98.9%
ValueCountFrequency (%)
129.26841
< 0.1%
129.48861
< 0.1%
129.53941
< 0.1%
129.57451
< 0.1%
129.60351
< 0.1%
ValueCountFrequency (%)
799.28191
< 0.1%
795.78951
< 0.1%
792.2481
< 0.1%
792.17521
< 0.1%
787.01141
< 0.1%

Elvirkningsgrad
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38859
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.69959493
Minimum10.541
Maximum64.9811
Zeros0
Zeros (%)0.0%
Memory size469.0 KiB
2021-03-08T18:33:06.602151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10.541
5-th percentile25.16049
Q129.4185
median32.3101
Q335.15265
95-th percentile41.0318
Maximum64.9811
Range54.4401
Interquartile range (IQR)5.73415

Descriptive statistics

Standard deviation5.734713307
Coefficient of variation (CV)0.1753756681
Kurtosis5.819061801
Mean32.69959493
Median Absolute Deviation (MAD)2.8673
Skewness1.391859793
Sum1962466.19
Variance32.88693672
MonotocityNot monotonic
2021-03-08T18:33:06.687729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.09221
 
0.4%
32.597563
 
0.1%
26.746754
 
0.1%
35.10546
 
0.1%
31.761744
 
0.1%
33.433343
 
0.1%
25.07542
 
0.1%
32.09640
 
0.1%
28.418337
 
0.1%
32.318936
 
0.1%
Other values (38849)59389
99.0%
ValueCountFrequency (%)
10.5411
< 0.1%
10.58721
< 0.1%
10.63451
< 0.1%
10.63551
< 0.1%
10.70531
< 0.1%
ValueCountFrequency (%)
64.98111
< 0.1%
64.9671
< 0.1%
64.94421
< 0.1%
64.93111
< 0.1%
64.91381
< 0.1%

Produktion total
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4864
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1305.944947
Minimum169
Maximum7600
Zeros0
Zeros (%)0.0%
Memory size469.0 KiB
2021-03-08T18:33:06.768914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum169
5-th percentile319
Q1589
median861
Q31686
95-th percentile3814.3
Maximum7600
Range7431
Interquartile range (IQR)1097

Descriptive statistics

Standard deviation1077.217272
Coefficient of variation (CV)0.8248565716
Kurtosis2.09494938
Mean1305.944947
Median Absolute Deviation (MAD)390
Skewness1.621168806
Sum78376286
Variance1160397.051
MonotocityNot monotonic
2021-03-08T18:33:06.855657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68479
 
0.1%
59578
 
0.1%
59878
 
0.1%
63377
 
0.1%
60177
 
0.1%
63876
 
0.1%
66375
 
0.1%
77175
 
0.1%
60774
 
0.1%
65574
 
0.1%
Other values (4854)59252
98.7%
ValueCountFrequency (%)
1691
< 0.1%
1811
< 0.1%
1831
< 0.1%
1841
< 0.1%
1851
< 0.1%
ValueCountFrequency (%)
76001
< 0.1%
69071
< 0.1%
67841
< 0.1%
65651
< 0.1%
60981
< 0.1%

Vejlys
Real number (ℝ≥0)

Distinct147
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.53091727
Minimum1
Maximum377
Zeros0
Zeros (%)0.0%
Memory size469.0 KiB
2021-03-08T18:33:06.939525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median17
Q332
95-th percentile64
Maximum377
Range376
Interquartile range (IQR)26

Descriptive statistics

Standard deviation21.23404264
Coefficient of variation (CV)0.9424402203
Kurtosis4.855142641
Mean22.53091727
Median Absolute Deviation (MAD)12
Skewness1.705729542
Sum1352193
Variance450.8845667
MonotocityNot monotonic
2021-03-08T18:33:07.025739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13928
 
6.5%
23327
 
5.5%
62151
 
3.6%
71938
 
3.2%
81929
 
3.2%
31910
 
3.2%
41905
 
3.2%
51816
 
3.0%
91705
 
2.8%
111518
 
2.5%
Other values (137)37888
63.1%
ValueCountFrequency (%)
13928
6.5%
23327
5.5%
31910
3.2%
41905
3.2%
51816
3.0%
ValueCountFrequency (%)
3771
< 0.1%
2451
< 0.1%
2271
< 0.1%
2091
< 0.1%
2041
< 0.1%

Eget forbrug
Real number (ℝ≥0)

Distinct377
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.66428393
Minimum5
Maximum1177
Zeros0
Zeros (%)0.0%
Memory size469.0 KiB
2021-03-08T18:33:07.107944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12
Q122
median30
Q346
95-th percentile136
Maximum1177
Range1172
Interquartile range (IQR)24

Descriptive statistics

Standard deviation45.98516737
Coefficient of variation (CV)1.029573595
Kurtosis24.18175845
Mean44.66428393
Median Absolute Deviation (MAD)10
Skewness3.755468407
Sum2680527
Variance2114.635618
MonotocityNot monotonic
2021-03-08T18:33:07.192567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
252009
 
3.3%
261980
 
3.3%
281960
 
3.3%
241948
 
3.2%
231935
 
3.2%
271829
 
3.0%
221821
 
3.0%
291744
 
2.9%
211701
 
2.8%
161637
 
2.7%
Other values (367)41451
69.1%
ValueCountFrequency (%)
5136
 
0.2%
6577
1.0%
71102
1.8%
8697
1.2%
9110
 
0.2%
ValueCountFrequency (%)
11771
< 0.1%
10051
< 0.1%
7311
< 0.1%
6891
< 0.1%
5901
< 0.1%

Date
Date

Distinct4076
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size469.0 KiB
Minimum2008-11-28 00:00:00
Maximum2020-11-30 00:00:00
2021-03-08T18:33:07.272175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:07.356075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

City
Categorical

HIGH CORRELATION

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size469.0 KiB
024 Qassimiut
 
3901
021 Saarloq
 
3685
013 Narsarmijit
 
3662
072 Napasoq
 
3417
035 Qassiarsuk
 
3232
Other values (31)
42118 

Length

Max length21
Median length13
Mean length12.96940765
Min length8

Characters and Unicode

Total characters778359
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row092 Attu
2nd row092 Attu
3rd row092 Attu
4th row092 Attu
5th row092 Attu
ValueCountFrequency (%)
024 Qassimiut3901
 
6.5%
021 Saarloq3685
 
6.1%
013 Narsarmijit3662
 
6.1%
072 Napasoq3417
 
5.7%
035 Qassiarsuk3232
 
5.4%
092 Attu3166
 
5.3%
124 Ilimanaq2928
 
4.9%
095 Iginniarfik2888
 
4.8%
169 Innarsuit2718
 
4.5%
182 Sermiligaaq2662
 
4.4%
Other values (26)27756
46.2%
2021-03-08T18:33:07.537903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
qassimiut3901
 
3.2%
0243901
 
3.2%
saarloq3685
 
3.1%
0213685
 
3.1%
narsarmijit3662
 
3.0%
0133662
 
3.0%
0723417
 
2.8%
napasoq3417
 
2.8%
qassiarsuk3232
 
2.7%
0353232
 
2.7%
Other values (63)84520
70.2%

Most occurring characters

ValueCountFrequency (%)
a91725
 
11.8%
60299
 
7.7%
i60169
 
7.7%
s58533
 
7.5%
r42433
 
5.5%
141496
 
5.3%
037819
 
4.9%
u36671
 
4.7%
t31192
 
4.0%
q25753
 
3.3%
Other values (28)292269
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter477716
61.4%
Decimal Number180045
 
23.1%
Space Separator60299
 
7.7%
Uppercase Letter60299
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
a91725
19.2%
i60169
12.6%
s58533
12.3%
r42433
8.9%
u36671
 
7.7%
t31192
 
6.5%
q25753
 
5.4%
k23298
 
4.9%
n22661
 
4.7%
l18880
 
4.0%
Other values (8)66401
13.9%
ValueCountFrequency (%)
141496
23.0%
037819
21.0%
222124
12.3%
517171
9.5%
313293
 
7.4%
612183
 
6.8%
911369
 
6.3%
88565
 
4.8%
48215
 
4.6%
77810
 
4.3%
ValueCountFrequency (%)
I14906
24.7%
N9725
16.1%
A9228
15.3%
S8636
14.3%
Q8430
14.0%
K6536
10.8%
T2195
 
3.6%
U359
 
0.6%
P284
 
0.5%
ValueCountFrequency (%)
60299
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin538015
69.1%
Common240344
30.9%

Most frequent character per script

ValueCountFrequency (%)
a91725
17.0%
i60169
11.2%
s58533
10.9%
r42433
 
7.9%
u36671
 
6.8%
t31192
 
5.8%
q25753
 
4.8%
k23298
 
4.3%
n22661
 
4.2%
l18880
 
3.5%
Other values (17)126700
23.5%
ValueCountFrequency (%)
60299
25.1%
141496
17.3%
037819
15.7%
222124
 
9.2%
517171
 
7.1%
313293
 
5.5%
612183
 
5.1%
911369
 
4.7%
88565
 
3.6%
48215
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII778359
100.0%

Most frequent character per block

ValueCountFrequency (%)
a91725
 
11.8%
60299
 
7.7%
i60169
 
7.7%
s58533
 
7.5%
r42433
 
5.5%
141496
 
5.3%
037819
 
4.9%
u36671
 
4.7%
t31192
 
4.0%
q25753
 
3.3%
Other values (28)292269
37.5%

City_code
Real number (ℝ≥0)

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.6574523
Minimum13
Maximum186
Zeros0
Zeros (%)0.0%
Memory size469.0 KiB
2021-03-08T18:33:07.607814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile13
Q135
median95
Q3153
95-th percentile182
Maximum186
Range173
Interquartile range (IQR)118

Descriptive statistics

Standard deviation58.1105487
Coefficient of variation (CV)0.6139035785
Kurtosis-1.396710239
Mean94.6574523
Median Absolute Deviation (MAD)60
Skewness0.09876336523
Sum5680867
Variance3376.83587
MonotocityNot monotonic
2021-03-08T18:33:07.684858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
243901
 
6.5%
213685
 
6.1%
133662
 
6.1%
723417
 
5.7%
353232
 
5.4%
923166
 
5.3%
1242928
 
4.9%
952888
 
4.8%
1692718
 
4.5%
1822662
 
4.4%
Other values (26)27756
46.2%
ValueCountFrequency (%)
133662
6.1%
161020
 
1.7%
18284
 
0.5%
213685
6.1%
243901
6.5%
ValueCountFrequency (%)
186682
 
1.1%
184393
 
0.7%
1831115
1.9%
1822662
4.4%
1711215
2.0%

Distrikt
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size469.0 KiB
005 Kujalleq
19372 
009 Avannaa
14090 
008 Disko
11081 
007 Qeqqata
9885 
004 Ilulisat
3254 

Length

Max length12
Median length11
Mean length10.89111056
Min length8

Characters and Unicode

Total characters653630
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row008 Disko
2nd row008 Disko
3rd row008 Disko
4th row008 Disko
5th row008 Disko
ValueCountFrequency (%)
005 Kujalleq19372
32.3%
009 Avannaa14090
23.5%
008 Disko11081
18.5%
007 Qeqqata9885
16.5%
004 Ilulisat3254
 
5.4%
006 Nuuk2333
 
3.9%
2021-03-08T18:33:07.838738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-08T18:33:07.889074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
kujalleq19372
16.1%
00519372
16.1%
00914090
11.7%
avannaa14090
11.7%
disko11081
9.2%
00811081
9.2%
qeqqata9885
8.2%
0079885
8.2%
ilulisat3254
 
2.7%
0043254
 
2.7%
Other values (2)4666
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0120030
18.4%
a84666
13.0%
60015
 
9.2%
l45252
 
6.9%
q39142
 
6.0%
e29257
 
4.5%
n28180
 
4.3%
u27292
 
4.2%
519372
 
3.0%
K19372
 
3.0%
Other values (17)181052
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter353555
54.1%
Decimal Number180045
27.5%
Space Separator60015
 
9.2%
Uppercase Letter60015
 
9.2%

Most frequent character per category

ValueCountFrequency (%)
a84666
23.9%
l45252
12.8%
q39142
11.1%
e29257
 
8.3%
n28180
 
8.0%
u27292
 
7.7%
j19372
 
5.5%
i14335
 
4.1%
s14335
 
4.1%
v14090
 
4.0%
Other values (3)37634
10.6%
ValueCountFrequency (%)
0120030
66.7%
519372
 
10.8%
914090
 
7.8%
811081
 
6.2%
79885
 
5.5%
43254
 
1.8%
62333
 
1.3%
ValueCountFrequency (%)
K19372
32.3%
A14090
23.5%
D11081
18.5%
Q9885
16.5%
I3254
 
5.4%
N2333
 
3.9%
ValueCountFrequency (%)
60015
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin413570
63.3%
Common240060
36.7%

Most frequent character per script

ValueCountFrequency (%)
a84666
20.5%
l45252
10.9%
q39142
9.5%
e29257
 
7.1%
n28180
 
6.8%
u27292
 
6.6%
K19372
 
4.7%
j19372
 
4.7%
i14335
 
3.5%
s14335
 
3.5%
Other values (9)92367
22.3%
ValueCountFrequency (%)
0120030
50.0%
60015
25.0%
519372
 
8.1%
914090
 
5.9%
811081
 
4.6%
79885
 
4.1%
43254
 
1.4%
62333
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII653630
100.0%

Most frequent character per block

ValueCountFrequency (%)
0120030
18.4%
a84666
13.0%
60015
 
9.2%
l45252
 
6.9%
q39142
 
6.0%
e29257
 
4.5%
n28180
 
4.3%
u27292
 
4.2%
519372
 
3.0%
K19372
 
3.0%
Other values (17)181052
27.7%

pop
Real number (ℝ≥0)

Distinct157
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.8943931
Minimum0
Maximum459
Zeros492
Zeros (%)0.8%
Memory size469.0 KiB
2021-03-08T18:33:07.962395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q153
median83
Q3184
95-th percentile262
Maximum459
Range459
Interquartile range (IQR)131

Descriptive statistics

Standard deviation97.34926419
Coefficient of variation (CV)0.8327966949
Kurtosis2.694094307
Mean116.8943931
Median Absolute Deviation (MAD)44
Skewness1.587817832
Sum7015417
Variance9476.879239
MonotocityNot monotonic
2021-03-08T18:33:08.047905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
831625
 
2.7%
521278
 
2.1%
801268
 
2.1%
2021103
 
1.8%
241089
 
1.8%
851081
 
1.8%
811024
 
1.7%
531019
 
1.7%
731004
 
1.7%
206987
 
1.6%
Other values (147)48537
80.9%
ValueCountFrequency (%)
0492
0.8%
2447
0.7%
437
 
0.1%
5505
0.8%
6559
0.9%
ValueCountFrequency (%)
459215
0.4%
457246
0.4%
456230
0.4%
453414
0.7%
448250
0.4%

Interactions

2021-03-08T18:32:58.344118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:58.417631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:58.492867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:58.567532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:58.638033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:58.710825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:58.782630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:58.852996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:58.923251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:58.993587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.064270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.136693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.208480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.278096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.349501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.420611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.491587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.562034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.629808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.704133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.776959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.852782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:32:59.928622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.005332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.082615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.159636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.241050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.319860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.398567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.477463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.560038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.641294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.723980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.804517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.885081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:00.963254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:01.045965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:01.123624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:01.197074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:01.274875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:01.350234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:01.427621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:01.506771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:01.584732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:01.795582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:01.866201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:01.941663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.016318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.099784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.179803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.253953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.330903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.406715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.482495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.555254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.629763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.703396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.782192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.860084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:02.935507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.014437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.090747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.165907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.238765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.311861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.383244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.459660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.535588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.609341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.684626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.759590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.833452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.902717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:03.974754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.048479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.126193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.203680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.278594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.357386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.433630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.507999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.579462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.651438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.720518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.793408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.865757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:04.938402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:05.010543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:05.081963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-08T18:33:05.151565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-03-08T18:33:08.120971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-08T18:33:08.402456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-08T18:33:08.499893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-08T18:33:08.600265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-08T18:33:08.685032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-08T18:33:05.512393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-08T18:33:05.657123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexBrændolie forbrugProduktion til byenGram pr. kWhElvirkningsgradProduktion totalVejlysEget forbrugDateCityCity_codeDistriktpop
01389.01442.0213.568639.44961530.09.074.02013-05-01092 Attu92008 Disko231
12316.01486.0167.048550.43571589.09.089.02013-05-02092 Attu92008 Disko231
23535.01588.0267.818831.45861678.08.075.02013-05-03092 Attu92008 Disko231
34401.01422.0222.777837.81891512.06.079.02013-05-04092 Attu92008 Disko231
45291.01369.0170.222849.49511436.06.055.02013-05-05092 Attu92008 Disko231
56455.01426.0258.767832.55891477.07.039.02013-05-06092 Attu92008 Disko231
67431.01482.0232.973036.16381554.06.060.02013-05-07092 Attu92008 Disko231
78392.01379.0225.534237.35661460.07.069.02013-05-08092 Attu92008 Disko231
89359.01268.0226.057037.27031334.05.056.02013-05-09092 Attu92008 Disko231
910391.01387.0223.733037.65741468.05.069.02013-05-10092 Attu92008 Disko231

Last rows

df_indexBrændolie forbrugProduktion til byenGram pr. kWhElvirkningsgradProduktion totalVejlysEget forbrugDateCityCity_codeDistriktpop
60005103129130.0345.0282.901629.7814386.08.032.02013-08-22095 Iginniarfik95008 Disko82
60006103130129.0351.0278.560430.2455389.09.031.02013-08-23095 Iginniarfik95008 Disko82
60007103131137.0347.0298.909128.1865385.09.028.02013-08-24095 Iginniarfik95008 Disko82
60008103132127.0350.0271.450431.0377393.010.035.02013-08-25095 Iginniarfik95008 Disko82
60009103133134.0362.0279.305230.1649403.09.031.02013-08-26095 Iginniarfik95008 Disko82
60010103134130.0324.0300.000028.0840364.010.030.02013-08-27095 Iginniarfik95008 Disko82
60011103135145.0356.0310.714327.1156392.09.028.02013-08-28095 Iginniarfik95008 Disko82
60012103136136.0373.0267.541031.4912427.010.042.02013-08-29095 Iginniarfik95008 Disko82
60013103137135.0367.0277.941230.3129408.011.031.02013-08-30095 Iginniarfik95008 Disko82
60014103138131.0339.0290.343029.0181379.010.030.02013-08-31095 Iginniarfik95008 Disko82